Image classification of germination potential of somatic embryos

ABSTRACT

The present invention is directed towards methods for the classification of plant embryos by the application of one or more classification algorithms to analyze digitized images and absorption, transmittance, or reflectance spectra. The methods are generally applicable and emphasize the importance of acquiring and using as much image and absorption, transmittance, or reflectance spectral information as possible, based on objective criteria. The present invention allows automated selection of embryos most suitable for further culture and rejection of those seen as less suitable.

CROSS-REFERENCES TO RELATED APPLICATIONS

This is a continuation of U.S. patent application Ser. No. 10/680,675,filed Oct. 7, 2003, which is a divisional of U.S. patent applicationSer. No. 09/700,037, filed Jul. 2, 2001, which is the National Stage ofInternational Application No. PCT/US99/12128, filed Jun. 1, 1999, whichin turn claims the benefit of U.S. Application No. 60/087,524, filedJun. 1, 1998.

FIELD OF THE INVENTION

The present invention relates to classification of plant embryos fordetermination of suitability for germination or other treatments. Inparticular, it is concerned with selection of conifer somatic embryosmost likely to be successfully germinated and to produce normal plants.

BACKGROUND

Reproduction of selected plant varieties by tissue culture has been acommercial success for many years. The technique has enabled massproduction of genetically identical selected ornamental plants,agricultural plants and forest species. The woody plants in this lastgroup have perhaps posed the greatest challenges. Some success withconifers was achieved in the 1970s using organogenesis techniqueswherein a bud, or other organ, was placed on a culture medium where itwas ultimately replicated many times. The newly generated buds wereplaced on a different medium that induced root development. From there,the buds having roots were planted in soil.

While conifer organogenesis was a breakthrough, costs were high due tothe large amount of handling needed. There was also some concern aboutpossible genetic modification. It was a decade later before somaticembryogenesis achieved a sufficient success rate so as to become thepredominant approach to conifer tissue culture. With somaticembryogenesis, an explant, usually a seed or seed embryo, is placed onan initiation medium where it multiplies into a multitude of geneticallyidentical immature embryos. These can be held in culture for longperiods and multiplied to bulk up a particularly desirable clone.Ultimately, the immature embryos are placed on a development ormaturation medium where they grow into somatic analogs of mature seedembryos. These embryos are then individually selected and placed on agermination medium for further development. Alternatively, the embryosmay be used in manufactured seeds.

There is now a large body of general technical literature and a growingbody of patent literature on embryogenesis of plants. Examples ofprocedures for conifer tissue culture are found in U.S. Pat. Nos.5,036,007 and 5,236,841 to Gupta et al.; U.S. Pat. No. 5,183,757 toRoberts; U.S. Pat. No. 5,464,769 to Attree et al.; and U.S. Pat. No.5,563,061 to Gupta.

One of the more labor intensive and subjective steps in theembryogenesis procedure is the selection from the maturation medium ofindividual embryos suitable for germination. The embryos may be presentin a number of stages of maturity and development. Those that are mostlikely to successfully germinate into normal plants are preferentiallyselected using a number of visually evaluated screening criteria.Morphological features such as axial symmetry, cotyledon development,surface texture, color, and others are examined and applied as apass/fail test before the embryos are passed on for germination. This isa skilled yet tedious job that is time consuming and expensive. Further,it poses a major production bottleneck when the ultimate desired outputwill be in the millions of plants.

It has been proposed to use some form of instrumental image analysis forembryo selection to replace the visual evaluation described above. Forexamples, refer to Cheng, Z. and P. P. Ling, “Machine Vision Techniquesfor Somatic Coffee Embryo Morphological Feature Extraction,” Trans.Amer. Soc. Agri. Eng. 37:1663-1669 (1994), or Chi, C. M., C. Zhang, E.J. Staba, T. J. Cooke, and W-S. Hu, “An Advanced Image Analysis Systemfor Evaluation of Somatic Embryo Development,” Biotech. and Bioeng.50:65-72 (1996). All of these methods require considerable pre-judgmentof which morphological features are important and the development ofmathematical methods to extract this information from the images.Relatively little of the information from the image has actually beenused.

The problem of how to best use image analysis to automate the selectionof somatic embryos after they had been separated from residual tissue,singulated, and imaged in color from multiple positions has not beensuccessfully addressed. Various methods are known for extracting sizeand shape information from scanned images. As one example, Moghaddam etal., U.S. Pat. No. 5,710,833, describes a method useful for recognitionof any multifeatured entity such as a human face. Sclaroff et al., U.S.Pat. No. 5,590,261, describes a method that can be used for objectrecognition purposes.

Where embryos are concerned, a further problem using scanning technologyis that morphology differs between clones within a given species. Thedifferences between acceptable and rejected embryos can be very subtle,varying by clone. Hence, the choice of selection criteria for machineuse tends to be subjective, difficult to specify mathematically, and maybe clone specific.

The development of high speed computers and new spectroscopic hardwarehas led to the development of new instruments which have the capabilityto rapidly acquire spectra on large numbers of samples. However, theacquisition of vast amounts of spectral data from a sample necessitatesthe development of similarly powerful data analysis tools to uncoversubtle relationships between the collected spectra and the chemicalproperties of the sample. One such data analysis methodology, commonlyknown as chemometrics, applies multivariate statistical techniques tocomplex chemical systems in order to facilitate the discovery of therelationship between the absorption, transmittance or reflectancespectral data acquired from a sample and some specified property of thesample that is subject to independent measurement. The end result ofmultivariate analysis is the development of a predictive classificationmodel that allows new samples of unknown properties to be rapidly andaccurately classified according to a specified property based upon theacquired spectral data. For example, multivariate analysis techniquessuch as: principal component analysis (PCA) and a principalcomponent-based method, projection to latent structures (PLS), have beenused to explore the multivariate information in previous applications ofnear-infrared (NIR) spectroscopy to the pulp and paper industry todevelop classification models for paper quality. See, for example, U.S.Pat. Nos. 5,638,284, 5,680,320, 5,680,321, and 5,842,150.

SUMMARY

The present invention is based on classification of plant embryos by theapplication of classification algorithms to digitized images andabsorption, transmittance, or reflectance spectra of the embryos. Themethods are generally applicable and emphasize the importance ofacquiring and using as much image and absorption, transmittance, orreflectance spectral information as possible, based on objectivecriteria. One goal has been automated classification and selection ofembryos most suitable for further culture and rejection of those seen asless suitable. The technique is capable of utilizing more compleximaging technology; e.g., multi-viewpoint images and images in color orfrom non-visible portions of the electromagnetic spectrum.

In one aspect of the present invention, a method for classifying plantembryos according to embryo quality is provided. The method firstdevelops a classification model by acquiring raw digital image data ofreference samples of plant embryos of known embryo quality. Optionally,the raw digital image data is preprocessed using one or morepreprocessing algorithms to reduce the amount of raw image data yetretain substantially all of the image data that contains geometric andcolor information regarding the embryo or embryo organ. An example ofsuch an optional preprocessing technique involves removing image datathat is not derived from the plant embryo or plant embryo organ. Anotheroptional preprocessing step results in the calculation of metrics whichemphasize image features that are particularly important in embryoquality classification. Data analysis is performed on the raw digitalimage data, or on the preprocessed image data depending upon whichmethod is followed, using one or more classification algorithms todevelop a classification model for classifying plant embryos by embryoquality. During this data analysis one or more of the classificationalgorithms utilizes raw digital image data representative of more thanjust the embryo perimeter, or the preprocessed image data to develop theclassification model. The embryo quality of the reference samples isdetermined by reference to such qualities as morphological comparison tonormal zygotic plant embryos, determination of the reference embryo'sconversion potential, resistance to pathogens, drought resistance andthe like. Raw digital image data of plant embryos of unknown embryoquality is then acquired using the same methods as performed on thereference samples. The acquired raw digital image data is then analyzedusing classification algorithms used to develop the classification modelin order to classify the quality of the plant embryo of unknown quality.A more robust method is obtained by acquiring raw digital image data ofmultiple views of the embryo, such as end-on views of the embryo and/orlongitudinal views.

In another aspect of the present invention plant quality is classifiedby developing a single metric classification model by acquiring rawdigital image data of reference samples of whole plant embryos or anyportion thereof from plant embryos of known embryo quality. A metricvalue is calculated from the acquired raw digital image data of eachembryo of known quality. The metric values are divided into two sets ofmetric values based upon the known embryo quality. A Lorenz curve iscalculated from each set of metric values. A threshold value isdetermined from a point on the Lorenz curve which serves as a singlemetric classification model to classify plant embryos by embryo quality.Raw image data is acquired from a whole plant embryo or any portionthereof from a plant embryo of unknown quality. The single metricclassification model developed from embryos of know quality is appliedto the raw image data acquired from plant embryos of unknown quality inorder to classify the quality of the unknown plant embryo. Single metricclassification models can optionally be combined using one or moreclassification algorithms to develop more robust classification modelsfor classifying plant embryos by embryo quality.

In another embodiment of the present invention, plant embryo quality isclassified by collecting absorption, transmittance or reflectancespectral raw data from plant embryos or portions thereof and processingthe data using classification algorithms. The inventive method firstrequires that a classification model be developed by acquiringabsorption, transmittance or reflectance spectral raw data of referencesamples of plant embryos or portions thereof whose embryo quality isknown. In one alternative embodiment, prior to making the classificationmodel, the spectral raw data in whole or in specific parts ispreprocessed to among other things, reduce noise and adjust for driftand diffuse light scatter. The classification model is then made byperforming a data analysis using classification algorithms on thepreprocessed spectral raw data. Absorption, transmittance or reflectancespectral raw data is then acquired from a plant embryo of unknown embryoquality. The spectral raw data collected from the embryo of unknownquality is either applied directly to the embryo quality classificationmodel or preprocessed to reduce noise and adjust for drift and diffuselight scatter and then the preprocessed spectral data is applied to theclassification model depending upon which method was used to make theclassification model in use. In either case, the application of theunknown spectral data to the classification model allows classificationof the quality of the plant embryo of unknown plant embryo quality.

DESCRIPTION OF THE DRAWINGS

The foregoing aspects and many of the attendant advantages of thisinvention will become more readily appreciated as the same becomesbetter understood by reference to the following detailed description,when taken in conjunction with the accompanying drawings, wherein:

FIG. 1 shows a diagrammatic representation of a tree embryo 8. Thecircled areas represent the embryo regions representative of the threeembryo organs known as cotyledon 10, hypocotyl 12, and radicle 14.

FIG. 2A displays a scoreplot obtained from principal component analysisof spectral data collected from Douglas-fir zygotic embryos of threedifferent developmental stages and a set of Douglas-fir somatic embryos(genotype 1). The units on the principal component (PC) axes areuniversal standard deviations for the set.

FIG. 2B shows the loadings spectra for each PC depicted in FIG. 2A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG.2A.

FIG. 3A displays a scoreplot obtained from principal component analysisof spectral data collected from loblolly pine zygotic embryos of twodifferent developmental stages and two sets of somatic embryos(genotypes 5 and 7). The units on the PC axes are universal standarddeviations for the set, and the crossover of zero axes is the averagebehavior of all the embryos.

FIG. 3B shows the loadings spectra for each PC depicted in FIG. 3A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG.3A.

FIG. 4A displays a scoreplot obtained from principal component analysisof spectral data collected from Douglas-fir somatic embryos at thecotyledonary stage (genotype 2) that have “good” and “poor” embryomorphology. The units on the PC axes are universal standard deviationsfor the set.

FIG. 4B shows the loadings spectra for each PC depicted in FIG. 4A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG. B.

FIG. 5A displays a scoreplot obtained from principal component analysisof spectral data collected from loblolly pine somatic embryos (genotype5) at the cotyledonary stage that have “good” and “poor” embryomorphology. The units on the PC axes are universal standard deviationsfor the set.

FIG. 5B shows the loadings spectra for each PC depicted in FIG. 5A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG.5A.

FIG. 6A displays a scoreplot obtained from principal component analysisof spectral data collected from Douglas-fir somatic embryos (genotype3). The scanned somatic embryos were of two different developmentalstages, the cotyledon stage and “dome” or “just cotyledon” stage. Theunits on the PC axes are universal standard deviations for the set.

FIG. 6B shows the loadings spectra for each PC depicted in FIG. 6A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG.6A.

FIG. 7A displays a scoreplot obtained from principal component analysisof spectral data collected from Douglas-fir somatic embryos (genotypes 3and 4). A set of somatic embryos from each genotype were eithersubjected to a cold treatment (which improves germination) or receivedno cold treatment (Control). The units on the PC axes are universalstandard deviations for the set.

FIG. 7B shows the loadings spectra for each PC depicted in FIG. 7A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG.7A.

FIG. 8A displays a scoreplot obtained from principal component analysisof spectral data collected from loblolly pine somatic embryos (genotypes5 and 7) at the cotyledonary stage. A set of somatic embryos from eachgenotype were either subjected to a cold treatment (which improvesgermination) or received no cold treatment (Control). The units on thePC axes are universal standard deviations for the set.

FIG. 8B shows the loadings spectra for each PC depicted in FIG. 8A. Eachcurve shows the relative contribution that each wavelength makes inaccounting for the variance depicted along the scoreplot axes in FIG.8A.

DETAILED DESCRIPTION

The inventive methods are used to classify any type of plant embryos,such as, for example, zygotic and somatic embryos, by any embryo qualitythat is amenable to characterization. For example, embryo quality can bedefined using morphological criteria such as axial symmetry, cotyledondevelopment, surface texture and color. As used herein “zygoticmorphology” refers to morphological criteria, such as axial symmetry,cotyledon development, surface texture and color that are characteristicof a normal zygotic plant embryo. Alternatively, embryos can beclassified using developmental or functional criteria, such as embryogermination and subsequent plant growth and development, oftencollectively referred to in the literature as “conversion.” As usedherein “conversion potential” refers to the capacity of a somatic embryoto germinate and/or survive and grow in soil, preceded or not bydesiccation or cold treatment of the embryo. In addition, “plant embryoquality” refers to other plant characteristics such as resistance topathogens, drought resistance, heat and cold resistance, salt tolerance,preference for light quality, suitability for long term storage ofsomatic embryos or any other plant quality susceptible toquantification.

Embryos from all plant species can be adapted to the inventive methods.The methods have particular application to agricultural plant specieswhere large numbers of somatic embryos are used to propagate desirablegenotypes such as with forest tree species. In particular, the methodscan be used to classify somatic embryos from conifer tree familyPinaceae, particularly from the genera: Pseudotsuga and Pinus. Adiagrammatic drawing of a Pseudotsuga tree embryo 8 is presented in FIG.1 in which the general locations of the three embryo organs, cotyledon10, hypocotyl 12, and radicle 14 are indicated.

In one embodiment of the present invention images of plant embryos orplant embryo organs are acquired in a digital form by scanning one ormore views of the embryos or organs from multiple positions using knowntechnology, such as electronic camera containing a charge couple devise(CCD) linked to a digital storage devise. A classification model forplant embryo quality is then developed by performing a data analysis onthe digital image data using one or more classification algorithms.Examples of such classification algorithms include, but are not limitedto, principal components analysis (see, for example, Jackson, J. E., AUser's Guide to Principal Components, John Wiley and Sons, New York(1991); Jolliffe, I. T., Principal Components Analysis, Springer-Verlag,New York (1986); Wold, S., “Pattern Recognition by Means of DisjointPrincipal Components Models,” Pattern Recognition 8:127-139 (1976); andWatanapongse, P. and H. H. Szu, “Application of Principal WaveletComponent in Pattern Classification,” Proceedings of SPIE, WaveletApplications V, H. H. Szu, Editor, vol. 3391, pp. 194-205 (1998)),Artificial Neural Networks (Mitchell, Tom M., Machine Learning,WCB/McGraw-Hill pp. 112-115, (1997)), Bayesian Classifiers (Mitchell at174-176), Probably Approximately Correct (PAC) Learning (Mitchell at203-220), Radial Basis Functions which includes the statisticaltechnique of fitting mixture distribution models to data (Mitchell, pp.238-240), and Nearest-Neighbor Methods (Mitchell at 231-236). Inaddition to the aforementioned classification algorithms, a newclassification algorithm is provided in the present invention toclassify plant embryos based upon the Lorenz curve. For a briefintroduction to Lorenz curves, see Johnson, S. and N. L. Kotz, Eds.Encyclopedia of Statistical Sciences, John Wiley, vol. 5, pp. 156-161(1985).

It is also well known in the art of data analysis that several differentalgorithms besides Principal Component Analysis (PCA) can be used todevelop and use classification models. More specifically, the followingstatistical techniques can also be adapted to the present invention:Partial Least Squares Regression, Principal Components Regression (PCR),Multiple Linear Regression Analysis (MLR), Discriminant Analysis,Canonical Correlation Analysis, Multivariate Multiple Regression,Classification Analysis, Regression Tree Analysis which includesClassification Analysis by Regression Trees (CART™, Salford Systems, SanDiego, Calif.), and Logistic and Probit Regression. See U.S. Pat. No.5,842,150 and (Mitchell, Tom M., Machine Learning, WCB/McGraw-Hill pp.112-115, 238-240 (1997)).

The classification model is deduced from a “training” data set ofmultiple images of plant embryos or plant embryo organs acquired fromembryos having known embryo quality. Embryos providing the training setimages are classified as acceptable or unacceptable based on biologicalfact data such as morphological similarity to normal zygotic embryos orproven ability to germinate or convert to plants. The inventive methodsare generally adaptable to any plant quality that is susceptible toquantification. Unclassified embryos are classified as acceptable or notbased on how close images of the unclassified embryos fit to theclassification model developed from the training set groups.

As used herein the term “classification algorithm” refers to anysequence of mathematical or statistical calculations, formulae,functions, models or transforms of image or spectral data from embryosused for the purpose of classifying embryos according to embryo quality.A classification algorithm can have just one step or many. In addition,classification algorithms of the present invention can be constructed bycombining intermediate classification models or single metricclassification models through the use of mathematical algorithms such asthe Bayes optimal classifier, neural networks or the Lorenz curve.Except for the single metric classification models, the imageclassification models of the present invention are derived from a dataanalysis of more than just embryo perimeter image data acquired fromplant embryos or embryo organs during the training sessions that lead tothe identification of an embryo quality classification model. That is,the classification models of the present invention, except for thesingle metric classification models, are developed using at least oneclassification algorithm which considers more of the acquired rawdigital image data than required to define the perimeter of the embryo.Thus, the classification algorithms perform a data analyses that resultsin the development of a classification model from the image or spectraldata without any subjective assumptions being made regarding which datafeatures are important for embryo quality classification.

As used herein “embryo perimeter” means the pixels in raw digital imagedata or preprocessed digital image data which define the outer perimeterof an imaged embryo.

Optionally, the raw digital image data can be preprocessed usingpreprocessing algorithms. As used hereafter the term “preprocessingalgorithm” refers to any sequence of mathematical or statisticalcalculations, formulae, functions, models or transforms of image orspectral data from embryos used for the purpose of manipulating image orspectral data in order to: 1) remove image or spectral data that isderived from non-embryo sources, i.e., background light scatter or othernoise sources; 2) reduce the size of the digital data file that is usedto represent the acquired image or spectra of the embryo while retainingsubstantially all of the data that represents informational featuressuch as geometric embryo shape and surface texture, color, and lightabsorption, transmittance or reflectance, of the acquired image orspectra; and 3) calculate metrics from the acquired raw image orspectral data and from values obtained during other preprocessing steps,in order to identify and emphasize embryo data that is useful indevelopment of an embryo quality classification model.

For example, U.S. Pat. No. 5,842,150 discloses that NIR spectral datacan be preprocessed prior to multivariate analysis using theKubelka-Munk transformation, the Multiplicative Scatter Correction(MSC), e.g., up to the fourth order derivatives, the Fouriertransformation or by using the Standard Normal Variate transformation,all of which can be used to reduce noise and adjust for drift anddiffuse light scatter.

Alternatively, the amount of digital data required to represent anacquired image or spectrum of an embryo can be reduced usingpreprocessing algorithms such as wavelet decomposition. See, forexample, Chui, C. K., An Introduction to Wavelets, Academic Press, SanDiego (1992); Kaiser, Gerald, A Friendly Guide to Wavelets, Birkhauser,Boston; and Strang, G. and T. Nguyen, Wavelets and Filter BanksWellesley-Cambridge Press, Wellesley, Mass. Wavelet decomposition hasbeen used extensively for reducing the amount of data in an image andfor extracting and describing features from biological data. Forexample, wavelet techniques have been used to reduce the size offingerprint image files to minimize computer storage requirements. Abiological example is the development of a method for diagnosingobstructive sleep apnea from the wavelet decomposition of heart beatdata. Wavelets enable rearrangement of the information in a picture ofan embryo into size and feature categories. For example, size and shapedata may be separated from texture. The results of a waveletdecomposition or functions thereof are then used as inputs to theclassification algorithms described above. A variety of otherinterpolation methods can be used to similarly reduce the amount of datain an image or spectral data file, such as, calculation of adjacentaverages, Spline methods (see, for example, C. de Boor, A PracticalGuide to Splines, Springer-Verlag, (1978)), Kriging methods (see, forexample, Noel A. C. Cressie, Statistics for Spatial Data, John Wiley,1993)) and other interpolation methods which are commonly available insoftware packages that handle images and matrices.

Other preprocessing algorithms can be used to process data collectedfrom an embryo in order to obtain the most robust correlation of theacquired data to embryo quality. For example, in Example 1 severalstatistical values were calculated to recapture some of the datainformation that was lost when a wavelet decomposition was used toreduce the size of the image. The recaptured information represented inthe metrics allowed the development of a classification model that wasbetter at predicting embryo quality than a model developed fromprincipal component analysis of image data that was preprocessed usingwavelet methods. As used hereinafter “metrics” refers to any scalarstatistical value that captures geometric, color, or spectral featureswhich contains information about the embryos, such as central andnon-central moments, function of the spectral energy at specificwavelengths or any function of one or more of these statistics. In imageprocessing language sets of metrics are also known as feature vectors.In addition, metrics can be derived from external considerations, suchas embryo processing costs, embryo processing time, and the complexityof an assembly line sorting embryos by quality.

In another embodiment of the present invention embryo regions arescanned and spectral data is acquired regarding absorption,transmittance or reflectance of electromagnetic radiation (hereinafterreferred to as light) at multiple discrete wavelengths ranging from 180nm to 4000 nm. Differences in spectral data collected from embryos ofhigh quality (for example, high conversion potential or highmorphological similarity to normal zygotic embryos) versus those of lowquality are presumed to reflect differences in chemical composition thatare related to embryo quality. Numerous studies assert that embryoquality is related to gross chemical composition of the embryo or itsparts, especially the amounts of water and storage compounds (proteins,lipids, and carbohydrates). Some examples include: Chanprame, S., T. M.Kuo, and J. M. Widholm, “Soluble Carbohydrate Content of Soybean [Gycinemax (L.) Merr.] Somatic and Zygotic Embryos During Development,” InVitro Cell Dev. Biol-Plant. 34:64-68 (1998); Dodeman, V. L., M. LeGuilloux, G. Ducreux, and D. de Vienne, “Somatic and Zygotic Embryos ofDaucus carota L. Display Different Protein Patterns Until Conversion toPlants,” Plant Cell Physiol. 39:1104-1110 (1998); Morcillo, F., F.Aberlenc-Bertossi, S. Hamon, and Y. Duval, “Accumulation of StorageProtein and 7S Globulins During Zygotic and Somatic Embryo Developmentin Elaeis Guineensis,” Plant Physiol. Biochem. 36:509-514 (1998); andObendorf, R. L., A. M. Dickerman, T. M. Pflum, M. A. Kacalanos, and M.E. Smith, “Drying Rate Alters Soluble Carbohydrates, DesiccationTolerance, and Subsequent Seedling Growth of Soybean (Glycine mac L.Merrill) Zygotic Embryos During In Vitro Maturation,” Plant Sci.132:1-12 (1998).

Spectrometric analysis of embryos can be performed using a datacollection setup that includes a light source, a microscope, a lightsensor, and a data processor. Preferably, each embryo region undergoesmultiple light scans in order to obtain a representative averagespectrum. In addition, it is useful that the data processor include abuilt-in calibration program which is run periodically throughout thedata collection phase to recalibrate the internal baseline to correctfor dark current, and to recalibrate against the standard whitebackground material upon which the embryo sits.

Preferably, the light sensor has a measuring interval of at the most 10nm, preferably 2 nm, and most preferably 1 nm or less. The detection oflight is performed in the ultraviolet, visible, and near infrared(including Raman spectroscopy) wavelength range of 180 nm to 4000 nm.This can be accomplished by the use of a scanning instrument, a diodearray instrument, a Fourier transform instrument or any other similarequipment, known to the person of skill in the art.

The classification of embryos according to quality (as defined above) bythe spectrometric measurements comprises two main steps. The first isthe development of a classification model, involving the substeps ofdevelopment of training and cross validating sets. Spectral data isacquired from embryos or embryo regions of known embryo quality,optionally a preprocessing of the acquired spectral data is performed,and then a data analysis is performed using one or more classificationalgorithms to develop a classification model for embryo quality. Thesecond main step is the acquisition of spectrometric data from an embryowhose quality is unknown, optionally performing preprocessing of theacquired spectral data, followed by data analysis of the acquiredspectral data using the classification model developed in the first mainstep.

Model training sets consist of a large number of absorption,transmittance or reflectance spectra acquired from embryos that have aknown high or low quality. The training sets are used in theclassification algorithms to develop a classification model. Aspreviously noted, a variety of preprocessing algorithms are availablethat can be used to first reduce noise and adjust for base line drift.However, for some data sets it may not be necessary to preprocess thedata to reduce background noise.

There are many data analysis methods that can be applied to develop anduse classification models that allow plant embryos to be classified byquality. The above described mathematical methods are a sampling of someof the major techniques. However, it should be emphasized that dataanalysis techniques can be put together in an almost infinite number ofcombinations to achieve the desired results. For example, a softindependent modeling of class analogy (SIMCA) method can be used onimages of embryos which have their color information collapsed into asingle array using principal components and then the result can beshrunk using wavelets. SIMCA can then be used to build principalcomponent regression models for each classification category. The Bayesoptimal classifier can then be used to combine the classificationdecisions from six SIMCA model pairs. Partial least squares regressioncan be used in place of principal component regression in the SIMCAstep. Similarly, neural networks can be used in place of Bayes optimalclassifier to combine classification decisions into a finalclassification model.

In addition, the methods described for classifying plant embryos usingembryo image data or absorption, transmittance or reflectance spectraldata can be combined together in a number of different ways. Forexample, data analysis of the acquired raw visual and spectral data canbe performed in parallel to develop a unitary classification model orthe analysis can be conducted in series whereby two independentclassification models are developed using the image and spectral dataseparately. Many permutations of the methods described herein arepossible to accomplish the classification of plant embryos by embryoquality.

The following nonlimiting examples illustrate the inventive methods andthe use of them to classify plant embryos that are most likely to besuccessfully germinated and produce normal plants.

Example 1 Mathematical Methods

There are three main steps in using light images to separate somaticembryos. They are: 1) cleaning the images to remove raw image data thatis not from the plant embryo or embryo organ; 2) reducing the amount ofraw image data acquired from the embryo or embryo organ while retainingas much embryo information as possible; and 3) applying one or moreclassification algorithms to develop and use a classification model forplant embryo quality.

Cleaning the Images

Image cleaning requires replacing the background in an image with zerosor pure black. The reason for this is to reduce variation betweenimages. It is desired that the only differences between images be due tothe embryos so that comparisons are not confounded with changes in thebackground. Since the images are magnified, slight variations inposition, reflections, glints off leftover material from previousembryos are magnified and contribute to the differences between theimages. Cleaning refers to the image processing steps used to eliminateall the variations in the background.

There is no set recipe for cleaning the embryo images since it isanticipated that as new imaging hardware and software are developed moresuitable image cleaning technique will evolve. However, severaltechniques are generally useful. The examples described below are merelyillustrative and are not meant to limit the present invention.

In the Examples that follow, the image of an embryo, its reflection onits stage and the remaining background were separated from each otherusing only the red component from the color image. The histogram of thered pixel values was positively skewed. A mixture distribution composedof three normal distributions was fit to the histogram by means of theEM algorithm. For a brief description of the EM algorithm see Mitchell,Tom M., Machine Learning, WCB/McGraw-Hill, pp. 191-196 (1997). The firstnormal picked up the background, the second normal picked up thereflection and the third component picked up the embryo. The mean of thesecond normal plus two times its standard deviation was used as theboundary between the reflection and the embryo. The red image wasthresholded at this value. The resulting binary image still had somepixels that belonged to the reflection included in it. These wereremoved by using morphological operations on the binary image. Usually,one to three erosions followed by the same number of dilations aresuccessful in cleaning up the image. Sometimes an extra couple ofdilations were needed to restore the embryo part of the binary image toits proper size. Any holes in the embryo part of the binary image werethen filled. The resulting binary image was then used to crop the colorimage and zero all non-embryo parts of the image. Each of the threecolor matrices in the original image were multiplied by the binary imageand then cropped to within two pixels of the embryo. This method workedfor all three views of the embryo.

Alternatively, a different method for cleaning each of the three embryoviews can be used. In this alternative method the longitudinal top viewof the embryo was preprocessed by first converting the red-green-bluevalues to hue. Saturation and intensity were not needed for this view.Taking the cotangent of 1/255^(th) of the hue flattened the range of thehue values making it easier to pick up more of the dark tail of theembryo. Only the positive hue values were used since most of thebackground ends up with negative or zero values for hue. Sometimes thepositive hue values alone were enough. A binary image was created bythresholding the cotangent values at 100. Values above 100 were setto 1. One erosion followed by two dilations eliminated the spuriouspixels from the background. The largest contiguous group of ones werekept as the embryo. Erosions and dilations were not done as many timesas in the previous method, in order to keep the radical or tail portionof the embryo image attached to the main embryo body. Hole filling wasdone before the erosion and dilations in order to maintain the radicalportion of the embryo image.

The longitudinal side view of the embryo (camera angle was rotated 90degrees relative to the top view) was preprocessed by creating a matrixof maximum color values. The maximum color values at a pixel was thelargest of the red, green, and blue color values. The maximum colorvalues were used to ensure maximum retention of the embryo radicalimage. The embryo had a horizontal position in this image. Therefore,the row average was calculated from the maximum color values. The lowestaverage value between rows 200 and 260 corresponded to the gap betweenthe embryo and the edge of the stage on which it sits. Everything belowthe row corresponding to the gap was set to zero. The rest of the imagewas thresholded so that values above ten were set to one. Again thebinary image was eroded once and dilated twice to remove spuriouspixels. A blob labeling routine labeled the remaining groups of pixelswith values of ones and the largest one was kept as the embryo. If asecond blob of ones had at least 25% of the number of pixels in it asthe largest blob then the radicle was assumed to have been separated bythe morphological operations and was included. Hole filling was done andthen the binary image was used to zero the background parts of theoriginal image and crop it as in the case of the top view.

The apical or end view of the embryo was preprocessed by one of twoways. The first method was to use the same method as described for theside view with three changes. After the stage part of the image was setto zero the remaining maximum values were thresholded at 20 instead of10. The resulting binary image was eroded 3 times and dilated 5 times.Finally, no second largest blob was kept. The second method was tocreate a binary image from the product of two other binary images. Thefirst binary image was created from the matrix of maximum values bysetting all values greater than 20 to one and zero otherwise. The secondbinary image was made by creating a matrix of hue values as for the topview and then setting the positive values to one and all others to zero.The product of these two binary images eliminates almost all backgroundfeatures. The resulting binary image was eroded and dilated as in thefirst method. Finally, the binary image was used to zero the backgroundand crop the original image as in the top view.

The reason the images were cropped was to concentrate later analyticaleffort on the embryo portion of the images as much as possible and toreduce the demands on computer memory. The three views of an embryorepresented three correlated measurements of a single experimental unit.It took hundreds of thousands of numbers to describe the measurements.The embryo only covers about 5% of the total area of an image, so mostof an image was background. Carrying along the background informationneedlessly uses up memory and can hamper later methods used to classifythe embryos.

Image Reduction

Since embryo image data sets are often large, further image sizereduction was performed in order to get the all of the data intocomputer memory. Also, the embryo classification algorithms that wereused to sort the embryos required that all of the images of a particularembryo view be the same size. The sizes of the largest top view, sideview and end-on view were found after all the images had beenpreprocessed and cropped as described in the preceding section. All topviews were zero padded out to the size of the largest top view with thecotyledon embryo head placed as close to one of the corners of the imageas possible. In other words, the extra zeros were added to the radicleend of the image and to one of the sides. Zero padding for the side andend views was similar. The zero padding scheme was performed in aneffort to get all the embryo heads in the same place in the images,while the radical tail portion of the embryo, which is highly variablein size and shape, were left to occupy what ever image space theyneeded.

With the images of each embryo view reset to the smallest common size,the images were then shrunk using wavelet computational methods. Thefirst step in reducing the images was to calculate the principalcomponents of the red, green, and blue color matrices pixelwise. Eachcolor matrix was strung out into a single long vector by appending thecolumns to each other. The first column was at the top of the vector andthe last column was at the bottom. The red, green, and blue vectors wereformed into a matrix with three columns and the singular valuedecomposition of this matrix was calculated. The left eigenvectors fromthe decomposition were principal components with unit length. The firsteigenvector corresponded to the principal component that accounted forthe most variation in the color values. On average the first principalcomponent (PC) accounted for 95% of the variation. The first PCrepresents the optimal weighted average of the red, green, and bluevalues for explaining variation and is similar to a calculated grayscalevalue. The first eigenvector was then reshaped into a matrix and wasused in place of the color array. This step reduced the computer memoryrequirements by ⅓ by replacing three matrices with a single matrix whosevalues were similar to a gray scale image. The single matrix carries allof the geometric information of the original. The second step was to doa two level two dimensional wavelet decomposition on the first PC imagein order to reduce its size. The approximationcoeffiecence[coefficient??] from the second level of the waveletdecomposition are used as the reduced image. The reduced image retainsat least 75% of the variability in the original PC image.

Metrics

Reducing the image data using the aforementioned methods means that someof the information in the original color data is lost. In an attempt tokeep some of this information, several statistics were calculated as thedata reduction process was performed. First, the mean standarddeviation, coefficient of skewness and coefficient of kurtosis werecalculated for each color as well as hue, saturation and intensity.Next, the coefficients of the wavelet decomposition at each scale weresummarized by their first five raw moments about zero. In a two leveldecomposition there are six matrices of detail coefficients and one ofsmooth coefficients. The detail coefficients contain information ontexture. The first five raw moments about zero were estimated for eachof these matrices as well as the smooth coefficients. The five momentsabout zero were the mean, mean squared value, mean cubed value, meanquartic value and mean quintic value. To obtain central moments like thevariance, skewness, etc., one subtracts the mean from the individualvalues first. However, central moments were more similar forclassification groups than for raw moments. A third set of statisticswere calculated from the perimeter of the embryo and its waveletdecomposition and are intended to quantify shape information.

The perimeter of the embryo was traced in a clockwise direction and therow and column coordinates of the edge pixels were obtained. The pixelcoordinates were interpolated to generate row and column vectors with1024 elements in each. Because many of the embryo perimeters wereconcave curves, equiangular interpolation could not be used. Instead,linear interpolation was used to create 1024 equally spaced coordinates.The coordinates were mean centered and then radii were calculated fromthem. When plotted in sequence the radii formed a lumpy sinusoid. Whenplotted in polar coordinates they traced the embryo. A ten level waveletdecomposition was performed on the radii and the first seven raw momentsabout zero were calculated for each level. A similar method has beenused by L. M. Bruce (“Centroid Sensitivity of Wavelet-based ShapeFeatures,” Proceedings of SPIE, Wavelet Applications V, Harold H. Szu,Editor, 3391:358-366 (1998)) to classify breast tumors as cancerous orbenign.

In addition to the moments of the wavelet coefficients from the radii,the area enclosed by the perimeter and it's length were calculated fromthe original coordinates. Also, the area and length of the convex hullof the perimeter were calculated. Lastly, the ratio of the perimeterarea to the convex hull area and the ratio of the perimeter length tothe convex hull length were calculated. If the embryo perimeter was aconvex curve, then the last two ratios will be unity. Otherwise, thearea ratio will decrease toward zero and the perimeter ratio willincrease.

In all, 142 metrics were described for the above embryo images. Thesemetrics were intended to capture some of the information on color, shapeand texture that is lost when the somatic embryo images are reduced insize. Some of the information such as the perimeter shape informationwas still in the reduced images. Adding the metrics the classificationmodel emphasizes the metrics information. In some analyses, (see Example4, Tables 2 and 3) the logarithm of the metric is taken to reducevariability.

Embryo Classification Models

Principal Component Analysis/SIMCA

The primary classification method used in the Examples of the presentinvention was soft independent modeling of class analogy SIMCA. SeeJolliffe, I. T., Principal Component Analysis, Springer-Verlag p. 161(1986). SIMCA was used on each set of reduced images and metrics. Thisresulted in six intermediate classification of each embryo. These sixintermediate classifications were combined using the Bayes optimalclassifier. See Mitchell, Tom M., Machine Learning, WCB/McGraw-Hill pp.174-176, 197, 222 (1997). SIMCA works by calculating a separate set ofprincipal components for each category based on training data. Theprincipal components which account for the majority of the variation arekept. Then data from a new sample is regressed on the principalcomponents from each group. The residual mean square errors arecalculated for each category. The category with the smallest residualmean square error is the category to which the new sample is assigned.Six SIMCAs are done for each embryo.

Combining the Intermediate Classifications Using the Bayes OptimalClassifier

Two to six or so intermediate classifications can be combined into asingle classification rule by first converting the resulting strings ofzeros and ones into a binary code. For two intermediate classificationsthere are four binary combinations, for three intermediateclassifications there are eight binary combinations, and so on. For ‘k’intermediate classifications there are 2^(k) binary combinations. Eachbinary combination is assigned a label or code. For each embryo qualityclass the probability of observing each code is estimated. Then theembryo-quality-class-by-binary-code probabilities are divided by theprobability of the corresponding code occurring in all the data fromboth embryo quality classes. The resulting probabilities are theconditional probability of an embryo quality class given a code. Anembryo's binary code is calculated and the embryo is assigned to theembryo quality class for which the conditional probability is highestfor the observed binary code. Ties can be assigned randomly or assignedto one of the embryo quality classes based on other considerations sucheconomics.

Using the Lorenz Curve for Classifying Embryos

Originally, the Lorenz curve was developed to compare incomedistribution among different groups of people. A Lorenz curve is createdby plotting the fraction of income versus the fraction of the populationthat owns that fraction of the income. In the present invention, theLorenz curve is viewed as a comparison of two paired cumulativedistribution functions where the fractional values of one cumulativedistribution function are plotted verses the fractional values of thesecond cumulative distribution function. If the two distributions arethe same the Lorenz curve will plot the straight line y=x. The pointfarthest from the line y=x corresponds to the balance point betweenaccumulating more of one distribution than the other. The balance orextreme point is an objective point at which to separate the twodistributions.

The Lorenz curve classification method of the present invention has foursteps. First, Lorenz curves are calculated for each metric in a set ofmetrics. The points on these Lorenz curves the furthest from the line,y=x, are found. Second, the metric values corresponding to the extremepoints on the Lorenz curves are used as the threshold values to makesingle metric classifications of the embryos: values of a metric lessthan its threshold are assigned to one embryo quality class and valuesgreater than the threshold are assigned to the other embryo qualityclass. Third, the set of metrics is subsetted to reduce the number ofcombinations that must be searched in the final stage. Fourth, pairs,triples, quadruples, etc., of the single metric classifications arecombined into binary codes and used in the Bayes optimal classifier tocreate classification models for assigning embryos to one of two qualityclasses. Classification models are made for all possible pairs, triples,quadruples, etc., and the best model is retained in each case.

Calculating the Lorenz Curve for a Single Metric

The metric values for the two embryo quality classifications arecombined and all the distinct metric values identified. Alternatively,the minimum and maximum value of all the metric values for both embryoquality classifications combined are found and a user specified numberof equally spaced steps between the minimum and maximum are used. Whenthere are too many distinct values, this second option is useful. Ineither case, for each distinct metric value, the fraction of metricvalues less than or equal to the distinct value is recorded for eachembryo quality class. Thus, two paired cumulative distribution curvesare obtained. Plotting these two sets of fractions against each otherconstitutes the Lorenz curve. If the two distributions are the same, theLorenz curve is the line, y=x.

Finding the Extreme Points on the Lorenz Curves

The distance of a point, (x₀, y₀) from the line, y=x, is the absolutevalue of the difference between y₀ and x₀ divided by the square-root oftwo: |y₀−x₀|/√{square root over (2)}. The absolute value of thedifference between the cumulative distribution functions of the twoclasses of embryo quality for a metric is searched for its highestpoint. The corresponding metric value is used as the threshold. Thisextreme point is the balance point between one distribution accumulatingmore probability than the other distribution. The extreme point was usedas the threshold in the metric classification models developed inExample 4. Other points on the Lorenz curve may be used as thresholdsbased on other considerations such processing costs. If a point otherthan the extreme point is used as the threshold, the Lorenz curve can beused to determine the tradeoff in miss-classification error rates.

Single Metric Classifications

Metric values less than the threshold are assigned to one of the embryoquality classes and values greater than the threshold are assigned tothe other quality class. These single metric classifications result inan embryo metric value being assigned a zero or one. This is done foreach metric used, one embryo quality class is set to one and the otheris set to zero. Several single metric classifications can then becombined to yield a final classification that has a lowermisclassification error rate than any of the individual single metricclassifications.

Combining the Lorenz Curve Single Metric Classifications Using the BayesOptimal Classifier

Two or more single metric classification models can be combined into asingle classification rule using the same Bayes optimal classifiermethod previously described to combine intermediate SIMCA classificationmodels. Alternatively, single metric classification models orintermediate SIMCA classification models can serve as the input data toneural network algorithm to arrive at a final classification model forplant embryo quality. However, as described below, when single metricclassification models are combined to arrive at a final classificationrule special problems arise.

Subsetting the Metrics to be Combined into a Single Classification Model

The Lorenz curve can be used to find an optimal threshold value for asingle metric. Optimal is here defined in the sense of balancingprobability accumulation. However, the Lorenz curve cannot handle thecase when several metrics are considered together because the Lorenzcurve can only compare two distributions at a time. One solution is tofeed sets of metrics into an artificial neural network to find anoptimal classification rule. However, with hundreds of metrics, it wouldbe necessary to either fit very large networks or fit a very largenumber of small networks. For the purpose of this application, thesimpler the classification rule the better. It is recognized that thethresholds found for individual metrics may not be the best ones to usewhen combining several metrics through their single metricclassifications. Nevertheless, it is possible to search large numbers ofcombinations of single metric classifications by calculating the resultsof the Bayes optimal classifier approach outlined above and comparingthem for various combinations of the single metric classifications. Yetthere are still limitations on the number of combinations that can besearched. When there are 682 metrics being considered, then there are8.935 billion distinct four-metric combinations alone. As computers getfaster such a number will not pose much of a problem. However, forlimited computing hardware, subsetting the metrics will greatly reducethe amount of work.

Two subsetting criterion present themselves. First, the metrics whosesingle metric classifications are above some limit can be kept. Second,many of the metrics are correlated with each other. The metrics highlycorrelated with the better metrics can be dropped from considerationsince they are informational twins to the better metrics: a metricperfectly correlated with another contains no information not already inthe other metric. Metrics with very low correlations among them are morelikely to create useful binary codes. These subsetting criterion can beused together to reduce the number of metrics.

Several different examples of classification techniques are specificallydemonstrated in the Examples 2-4.

Example 2 Somatic Embryo Sorting Based Upon Visual Embryo Quality

Douglas-fir somatic embryos were cultured to the cotyledon stage by themethods outlined in Gupta et al., U.S. Pat. No. 5,036,007, and Gupta,U.S. Pat. No. 5,563,061, which patents are herein incorporated in theirentirety by reference. Embryos were individually removed from thedevelopment stage medium. From this point they would normally bemanually screened and selected for germination.

In the present case, two hundred embryos from the same clone ofDouglas-fir genotype 5 were preselected by morphology using the usualzygotic embryo criteria of color, axial symmetry, freedom from obviousflaws, and cotyledon development. Half of the sample was considered tobe “good” embryos; i.e., embryos that met visual criteria for furtherprocessing in germination medium. The other half were “bad” embryos thatdid not meet the criteria, The “truth criterion” for the followinganalysis was the presence or absence of normal zygotic-like morphology.

After selection, the embryos were placed against a dark background andilluminated by cool fiber optic light. Each embryo was individuallycolor-imaged in rapid sequence by three cameras mounted perpendicular toeach other. Two longitudinal views 90° to each other and an apicalend-on view of the cotyledon region were acquired. Images were acquiredas digitized data suitable for computer analysis. Prior to analysis theimages were preprocessed to isolate the embryo and thus eliminateinterfering background data.

In this example, a subset of the embryo top view images were used tocalculate the principal components. The first 80 components were kept asthey account for about 98% of the variation in the images. Principalcomponents were calculated for the “good” embryos, i.e., those embryosthat possess good visual criteria that are associated with a highgermination rate, as well as for embryos that lack the good visualfeatures. The principal components were calculated using the singularvalue decomposition algorithm. The singular value decompositionalgorithm is available with any software capable of handling matrices.The principal components used were the left eigenvectors from thesingular value decomposition which were the principal componentsnormalized to have unit length. This normalization process does not havean adverse effect because the principal components were being used inthis method as a set orthogonal basis vectors in a multiple regression.The embryos that were not included in the training data set were thenregressed on the two sets of principal components exactly as done inmultiple regression. For each regression the residual mean square errorwas calculated. A test embryo was classified as having either good orbad embryo visual quality depending on which category has the smallerresidual mean square error. Using this method test embryos wereclassified based on the longitudinal top view of an embryo.

Similar to the longitudinal top view images, the longitudinal side viewand end view images were divided into a training set and test set ofembryos. The training set of embryos were used for calculating theprincipal components and the test set of embryos were regressed on themand classified. Likewise, the metrics were used to calculate principalcomponents and classify the embryos in the test set. In the case of themetrics, 40 principal components were kept and they were based on thenatural logarithm of the absolute value of the metrics multiplied by thesign of the metric or the Box-Cox transformation (Myers, R. H. and D. C.Montgomery, Response Surface Methodology: Process and ProductOptimization Using Designed Experiments, Wiley, pp. 260-264 (1995)) ofthe metrics using an odd root such as a 1/101 which approximates thenatural logarithm, preserves the sign, and still works on zero. Thetransformation helps reduce the variability of the higher order moments.As a result each embryo in the test set ends up with six classificationsfrom each of the SIMCAs: three classifications from the three images andthree classifications from the three sets of metrics.

The six classifications were combined into a single classification usingBayes optimal classifier as follows. See Mitchell, T. M. MachineLearning, WCB/McGraw-Hill, pp. 174-176, 197, 222 (1997). Eachclassification was either zero or one: one meaning that the embryo had agood visual quality and zero meaning that the embryo did not have goodvisual characteristics. These six binary classification scores wereconverted to a multi-valued code by multiplying the side view imagescore by 32 and adding it to 16 times the end view image score plus 8times the top view image score plus 4 times the side view metric scoreplus 2 times the end view metric score plus the top view metric score.This composite score takes on integer values ranging from 0 to 31. Foreach composite score, the number of good visual quality embryos werecounted as well as the number of bad visual quality embryos. Dividing bythe total number of embryos in the test set yields the probabilities ofobserving each score and one of the embryo categories. The probabilityof each composite score occurring was calculated by counting how manytimes each score occurred and dividing by the total number of embryos inthe test set. Next, each probability of observing a composite score andone of the categories was divided by the probability of the compositescore occurring. This calculation gave the probability of a categorygiven a composite score. Composite scores where the probability ofobserving a visually correct embryo was greater than or equal to 50%were assigned as having a good embryo quality. All other scores wereassigned to the bad embryo quality category. In this way the informationfrom the six SIMCA classifications were combined into a singleclassification.

Basically, the Bayes optimal classifier assigns a composite score to thecategory which generates the most of that particular score. If an embryohas a value that is in the middle it was put into the good embryoquality category. The whole process was repeated many times and theaverage performance reported.

Using the above methods two additional sets of somatic embryos of twodifferent genotypes (genotypes 6 and 7) were classified as having goodor bad morphological qualities as compared to normal zygotic embryos.The results of the three sets are given in Table 1.

TABLE 1 Visual Quality Classification Results From the Bayes OptimalClassifier for Three Genotypes of Douglas-Fir Somatic Embryos Percent ofEmbryos Percent of Embryos Classified Correctly as Correctly Classifiedas Having “Good” Visual Having “Bad” Visual Douglas-fir Genotype EmbryoQuality Embryo Quality 5 (Three views of 80.0 75.0 200 embryos) 6 (Threeviews of 88.7 70.5 1000 embryos) 7 (End & Top views of 87.0 78.5 1000embryos)

Example 3 Somatic Embryo Sorting Based Upon Visual Embryo Quality andActual Germination

A sample of 400 embryos judged to be of high morphological quality, aspreviously defined, from the Douglas-fir genotype 5 was evaluated in twoways. After evaluation the embryos were germinated to determine whethergermination success correlated with predicted success based on eightadditional morphological features. The base case was visual selectionbased on morphology. The first procedure was a nonparametric statisticaltreatment based on four observed features (symmetry, surface roughness,presence of fused cotyledons, and presence of gaps between cotyledons)and four measured embryo dimensions (hypocotyle length, radical length,cotyledon length, and cotyledon number) the measurements being made ondigital color images acquired under sterile conditions from a singleviewpoint perpendicular to the long axis of the embryo. This statisticalprocedure is known as binary recursive classification and was carriedout using software named CART™ (for Classification and Regression Tree)(Salford Systems, San Diego, Calif.). Reliability of this classificationmethod was assessed and probabilities for future similar data sets werederived by validating the classification on a specified number; e.g.,20, random subsets of the data. CART™ classification is binary and allpossible splits were tested on all variables. The second evaluationmethod was principal components analysis of the images.

Results showed principal components analysis was superior to the CART™statistical procedure and was a major improvement over technicianselection. A 66.3% germination rate was found for the base populations(selected for good similarity to normal zygotic embryos). This improvedto 75.0% for embryos classified by the CART™ procedure as most likely togerminate. A germination success of 79.7% was achieved in embryos chosenby the principal components/SIMCA analysis method.

Example 4 Somatic Embryo Sorting Based Embryo Germination A Comparisonof Classification Methods

The methods in Examples 1-3 were used to develop classification modelsand classify 1000 somatic embryos of Douglas-fir genotype 6 by theircapability to germinate. Table 2 contains the results of presentingdifferent inputs to the Bayes optimal classifier when classifying thegermination versus nongermination capabilities of the Douglas-firgenotype 6 embryos. When the data input was somatic image data that wasfirst preprocessed using the method of Example 1 the training set modelfor the classification of embryos by germination was accurate 59% of thetime at correctly classifying embryos as embryos that would germinateand about 64% accurate at classifying embryos that would not germinate.This is an average accuracy of 61.7%. In contrast, when metrics imagedata was captured and added to the preprocessed image data following themethods in Example 1, the accuracy of embryo classification intogerminating and non-germinating embryos was increased to about 71%(column 4 of Table 2). Thus, as in Example 2, an increased accuracy inclassifying potential germinants was achieved using the presentinvention.

TABLE 2 Germination Classification of Douglas-Fir Genotype 6 SomaticEmbryos Using Different Inputs to Bayes Optimal Classifier Compared WithGermination Results of Manual Selection Based on Morphology Percent ofPercent of Combinations of Germinating Non-Germinating Average SIMCAResults Embryos Embryos Success Used in Correctly Correctly in BayesOptimal Classified as Classified as Classifying Classifier GerminatingNon-Germinating Correctly Images Only 59.3 64.1 61.7 Images + Metrics67.6 74.6 71.1 Images + Log (Metrics) 68.5 74.1 71.3 Manual SelectionBased 71.7 66.2 68.9 on Morphology

Table 3 presents the germination classification results for Douglas-firgenotype 6 of the individual SIMCA runs from each set of images andmetrics of the somatic embryos. Comparing the results presented in Table3 with those shown in Table 2 demonstrates the statistical advantage ofcombining the individual SIMCA classifications using the Bayes optimalclassifier of each of three different somatic embryo views. Also, theutility of adding the metrics is illustrated.

TABLE 3 Germination Classification of Douglas-Fir Genotype 6 SomaticEmbryos: Results From the Individual SIMCA Runs Percent of Percent ofGerminating Non-Germinating Embryos Correctly Embryos CorrectlyClassified as Classified as Data Used Germinating Non-Germinating TopView Images 66 54 Top View Log(Metrics) 46 63 End View Images 70 45 EndView Log(Metrics) 52 52 Side View Images 48 59 Side View Log(Metrics) 5253Additional Classification Methods

Two additional classification methods were performed with data collectedfrom somatic embryos: neural networks (Douglas-fir genotype 6) and aclassification method based on the Lorenz curve (Douglas-fir genotypes 6and 7). The method based on SIMCA uses hyperplanes as boundaries betweencategories. A two dimensional hyperplane is a line and a three dimensionhyperplane is a regular plane or flat surface. In short, hyperplanes arejust higher dimensional cousins to lines and regular planes. As a resultthey are best for separating categories that are linearly separable,i.e., they have straight boundaries and can be separated by a “line”.Often nature does not have linear boundaries but very curved boundaries.Simple back-propagation neural networks using nonlinear transferfunctions for the hidden nodes and output nodes can handle verynonlinear boundaries between categories. See Hagan, M. T., H. B. Demuth,and M. Beale, Neural Network Design, PWS Publishing Company, Chapters 11and 12 (1996). These have been used to discriminate between images ofpeople looking in different directions. Id. pp. 112-115.

Neural Network

Back-propagation neural networks were used to classify embryos ofgenotype 6 as germinating or non-germinating. The end view and top viewsomatic embryo images were reduced in size by wavelets in order toreduce the number of network input nodes as was suggested by T. M.Mitchell (Machine Learning, WCB/McGraw-Hill, pp. 112-115 (1997)).Mitchell used adjacent averages to reduce his images. Here the smoothcoefficients from the 3^(rd) level of the two-dimensional waveletdecomposition were used since they preserve much more detail thanaverages. The embryo side view was not included to reduce the amount ofcomputation and because as shown in Table 3 this view carries the leastamount of information about germination of three views. The input layerof the network just fed in the pixel values from the reduced images fromboth views. The hidden layer had either 18 or 80 hidden nodes using thelogistic transfer function, 1/(1+exp(−x)). The output layer had twonodes again using logistic functions. The output target values were(0.9, 0.1) for germinating somatic embryos and (0.1, 0.9) fornon-germinating embryos. The sum of the squared differences between thetarget vectors and their predicted vectors were minimized. Half the datawas used for training and half was used for validation. Any training setand even all of the embryos could be perfectly classified with the 18hidden node model. The best either of the neural network models could doon a validation or test set was 61% correct classification of embryosinto both the germinating and non-germinating classes.

Use of the Lorenz Curve Classification Method to Classify Embryos

As previously noted the Lorenz curve classification method has foursteps. In this example, 625 and 457 different metrics were calculatedfor Douglas-fir genotypes 6 and 7, respectively. Metric valuescorresponding to the extreme points on the Lorenz curves for each metricwere set as threshold values for classifying embryo quality. Inaddition, the set of single metric classifications which were searchedfor robust combination classification models was reduced using thesubsetting routine described in Example 1. Lastly, double, triple,quadruple, etc., combinations of the single metric classification modelswere combined into binary codes and used in the Bayes optimal classifierto create classification rules for assigning embryos to one of the twoembryo quality classes. Classification models were made for all possiblepairs, triples, and quadruples and the best model was retained in eachcase.

Table 4 contains the results of classifying embryos according to theirmorphological similarity to normal zygotic embryos by using the LorenzCurve classification method combining 1, 2, 3, and 4 single metricclassifications via the Bayes optimal classifier.

TABLE 4 Morphology Classification Results From the Best Bayes OptimalClassifier Combining 1, 2, 3 & 4 Lorenz Curve Single MetricClassifications for Douglas-Fir Genotypes 6 and 7 Percent of GoodPercent of Bad Morphology Morphology Number of Metrics Embryos CorrectlyEmbryos Correctly Used to Create Classified as Having Classified asHaving Douglas-fir Genotype Classification Model Good Morphology BadMorphology 6 1 82.30 70.44 (end, side & top views) (Skewnesscoefficient, β₁, of all the intensity pixel values from the embryo endview) 6 2 72.63 83.27 (end, side & top views) (Skewness coefficient, β₁,of all the intensity pixel values from the embryo end view, and Range ofthe perimeter radii from the embryo end view) 6 3 79.69 78.96 (end, side& top views) (Skewness coefficient, β₁, of all the intensity pixelvalues from the embryo end view, range of the perimeter radii from theend view, and standard deviation of the area of the cotyledons from theembryo end view) 6 4 84.72 75.75 (end, side & top views) (Skewnesscoefficient, β₁, of all the intensity pixel values from the embryo endview, range of the perimeter radii from the end view, standard deviationof the area of the cotyledons from the embryo end view, and mean area ofthe cotyledons touching the bounding convex hull of the embryo end view)7 1 88.59 71.61 (end & top views only) (Lower quartile of the perimeterradii from the embryo top view) 7 2 71.33 89.74 (end & top views only)(Lower quartile of the perimeter radii from the embryo top view andskewness coefficient, β₁, of the blue pixel values from the embryo endview) 7 3 85.71 84.97 (end & top views only) (Skewness coefficient, β₁,of all the blue pixel values from the end view, standard deviation ofall the green pixel values from the end view, and 4^(th) moment aboutzero of the detail coefficients of the 8^(th) level of a 10 levelwavelet decomposition of the embryo end view perimeter) 7 4 85.10 87.05(end & top views only) (Skewness coefficient, β₁, of all the blue pixelvalues from the end view, standard deviation of all the green pixelvalues from the end view, 4^(th) moment about zero of the detailcoefficients of the 8^(th) level of the wavelet decomposition of the endview perimeter, and lower quartile of the perimeter radii from theembryo top view)

Comparing the results in Table 4 with the corresponding results in Table1 from combining 6 SIMCA intermediate classifications by the Bayesoptimal classifier suggests that the Lorenz curve based method performsas well as or better than the SIMCA based method for classifying embryosaccording to morphology. Similarly, Table 5 contains the results fromclassifying embryos according to germination classes by the Lorenz curvemethod. Comparing Table 5 with Table 2 shows that the Lorenz curvemethod does not perform as well as the SIMCA based method. Also, Table 4and Table 5 show that combining the information in multiple metricsreduces the misclassification error rate.

TABLE 5 Germination Classification Results From the Best Bayes OptimalClassifier Combining 1, 2, 3 & 4 Lorenz Curve Single MetricClassifications for Douglas-Fir Genotype 6 Percent of Percent ofGerminating NonGerminating Douglas-fir Embryos Embryos Genotype usingNumber of Metrics Correctly Correctly (end, side & Used to CreateClassified as Classified as top views) Classification Model GerminatingNonGerminating 6 1 70.51 60.12 (Skewness coefficient, β₁, of all theblue pixel values from the embryo end view) 6 2 66.51 65.45 (Skewnesscoefficient, β₁, of all the blue pixel values from the embryo end view,and 10^(th) level detail coefficient from a 10 level waveletdecomposition of the embryo side view perimeter) 6 3 71.56 62.40(Skewness coefficient, β₁, of all the blue pixel values from the embryoend view, kurtosis coefficient, β₂, the perimeter radii from the embryotop view, and mean of the level 9 detail coefficients from a 10 levelwavelet decomposition from the embryo side view perimeter) 6 4 65.3370.70 (Skewness coefficient, β₁, of all the blue pixel values from theembryo end view, kurtosis coefficient, β₂, the perimeter radii from theembryo top view, mean of the level 9 detail coefficients from a 10 levelwavelet decomposition from the embryo side view perimeter, and kurtosiscoefficient, β₂, of all the green pixel values from the embryo sideview)Classification Trees Based on the Lorenz Curve

An alternative method for classifying embryos uses Lorenz curve as themethod for splitting nodes in classification trees. Usually to constructa classification tree the metrics are searched to find a variable thatseparates the quality classes the most based on a measure of distance orspread. Multivariate statistics can also be used to examine sets ofmetrics, however, the computation required increases rapidly with thenumber of metrics in a set. The Lorenz curve method outlined above canalso be used as a node splitting criterion. The Lorenz curve methodoutlined above was used to search for a single best metric to split theembryo quality classes. The two subsets thus created were each submittedto the Lorenz method to find a metric that best split them. This processcan be repeated as long as the number of metric values from each embryoquality class are large enough to provide a good estimate of thedistribution functions. The entire set of metrics is searched each timebecause the act of splitting the distributions, alters thedistributions, and metrics that at first provided poor separation mayprovide good separation at later stages. This method of method ofcreating a classification tree is very computationally intensive. As aresult the metrics can be subsetted in order to get the computationsdone in a reduced time. A two level classification tree based on theLorenz curve was created for Douglas-fir genotype 7. The results are inTable 6.

TABLE 6 Morphology Classification Results From a Two LevelClassification and Regression Tree Using Lorenz Curves to Split Nodesfor Douglas-Fir Genotype 7 Percent Percent of Good of Bad MorphologyMorphology Embryos Embryos Correctly Correctly Douglas-fir ClassifiedClassified Genotype 7 using Number of Metrics as Having as Having (end &top views Used to Create Good Bad only) Classification Model MorphologyMorphology 2 81.22 82.25 (Standard deviation of all the red pixel valuesfrom the embryo end view, and 2^(nd) moment about zero of all the pixelvalues in the 1^(st) principal component image (the view created bycollapsing the red, green, and blue color matrices into a single matrixusing principal components) of the end view)

The techniques described in Examples 1-4 can be readily adapted tocontinuous examination of somatic embryos as might be required in alarge scale production facility. In addition, these methods can becombined in series with themselves or with the spectroscopy methodsdescribed in Example 5 to create an efficient and cost effectivescreening methodology for classifying somatic embryos by theirgermination potential.

Example 5 Spectrophotometric and Multivariate Methods for ClassifyingSomatic Embryos

Spectral data was collected and analyzed from zygotic and somaticembryos populations that from experience are known to differconsiderably in germination vigor.

Zygotic Embryos

Fresh zygotic embryos were collected at two intervals about three weeksapart from one orchard grown Douglas-fir tree (Pseudotsuga menziesii).The degree of embryo development corresponded to Stages 7 and 8a in theclassification published by Pullman et al. (Pullman, G. S. and D. T.Webb, “An Embryo Staging System for Comparison of Zygotic and SomaticEmbryo Development,” Proc. TAPPI [Technical Association of the Pulp andPaper Industry] Biological Sciences Symposium, Minneapolis, Minn., Oct.3-6, 1994, pp. 31-33. TAPPI Press, Atlanta, Ga. (1994)) for the July 23and August 13 collections, respectively. These stages may be describedas “just cotyledonary” and “cotyledonary, immature.” In addition, fullymature zygotic embryos were obtained from mature seed obtained from aseed store collected from a mix of different trees grown in the sameorchard. Immature loblolly pine (Pinus taeda) zygotic embryos werecollected from one tree on August 10, at which date they were at Stage 7in Pullman et al.'s classification system cited above. Mature loblollypine seed embryos were obtained from freezer storage, and the decoatedseed allowed to imbibe water for 14 hours before extraction of theembryos for analysis. Cones and seed were stored at 4-6° C. aftercollection until spectral analysis was performed.

Somatic Embryos

Douglas-fir somatic embryos of four different genotypes, designated 1,2, 3, and 4, were analyzed in this study. The Douglas-fir somaticembryos were cultured as described in Example 2. Where a cold treatmentis noted, the Douglas-fir somatic embryos received cold treatment at4-6° C. for four weeks prior to spectral analysis. Two genotypes ofloblolly pine somatic embryos were used in the study, designatedgenotypes 5 and 7. After completing their development to thecotyledonary embryo stage on petri plates, half of the somatic loblollypine embryos from each genotype received a partial drying treatment for10 days at about 97% relative humidity while still on the culturemedium, followed by cold treatment at 4-6° C. for four weeks. The otherhalf of the loblolly somatic embryos did not receive this treatment. Theloblolly somatic embryos were produced using standard somatic embryoplating methods described in Gupta et al., U.S. Pat. No. 5,036,007 andGupta, U.S. Pat. No. 5,563,061.

For each population, spectral analysis was performed on about 10 embryosexcept for some somatic embryos where spectral data was collected fromabout 15-40 embryos. Spectra were taken usually from the cotyledonregion of an embryo (FIG. 1). However, it should be understood that theinventive method can be practiced by collecting spectral data from theentire embryo or from the hypocotyl (12) or radical (14) portions of theembryo as diagrammed in FIG. 1. In some instances the classification wasimproved by using both cotyledon (10) and radical (14) data in sequence.

Collection of Spectral Data

The experimental setup consisted of a light source, a binocularmicroscope, a NIR sensor, and a portable NIR processor with computer. AFieldSpec FR (350-2500 nm) Spectrometer (Analytical Spectral Devices,Inc., Boulder, Colo.) equipped with a fiber optic probe which gatherslight reflected from any surface was used to collect embryo spectraldata. The fiber optic probe of the spectrometer was fitted with a 5degree fore-optic and inserted into the auxiliary observation (camera)port of a binocular microscope.

Spectra were acquired sequentially from groups of ten somatic embryosimmediately after hand-transferring from a culture plate, and fromzygotic embryos on a one-by-one basis immediately after excision fromdecoated seeds using the apparatus and procedures described below. Thehalogen lamp was set at 40 degree angle from the vertical at a distanceof 17 cm from the embryos. Samples were placed on a white Teflon surfaceto minimize background absorption while being viewed with the 6.5×, 10×,or 40× microscope objective. A “white balance” program that is part ofthe spectrometer, was run periodically throughout the measurements torecalibrate the instrument against the white background when no embryoswere present.

Spectra were measured in the region from visible to very near IR range(350 to 2500 nm). Spectral intensities were measured at 1 nm increments.The spectrometer was programmed to complete 30 spectral scans of eachembryo in order to obtain a representative average spectrum—a processwhich took a total of 30 seconds per embryo for separate cotyledon andradical sampling, including the time to reposition for the next embryo.

Data Processing and Information Extraction

Analysis of spectral data was performed using a Principal ComponentAnalysis software package (“The Unscrambler” by Camo ASA, Oslo, Norway).The scores and loadings matrices were converted to the “scoreplots” and“loadings spectra” shown in the figures. The principal componentanalysis algorithm extracted the best set of axes that described thedata set. The scoreplots show the relationships among the embryos, andembryo classes, while the loadings spectra show which spectral featureswere responsible for the class distinctions.

Principal Component Analysis of Spectra From Zygotic and Somatic Embryos

A comparison of Douglas-fir zygotic embryos of three differentdevelopmental stages and somatic embryos from Genotype 1 was performed.The three zygotic stages consisted of two immature cotyledonary stages,identifiable as stages 7 and 8 in Pullman et al. (Pullman, G. S. and D.T. Webb, “An Embryo Staging System for Comparison of Zygotic and SomaticEmbryo Development,” Proc. TAPPI [Technical Association of the Pulp andPaper Industry] Biological Sciences Symposium, Minneapolis, Minn., Oct.3-6, 1994, pp. 31-33. TAPPI Press, Atlanta, Ga. (1994)) collected fromthe field in Rochester, Wash., on July 23 and August 14, respectively,and mature dry seed from a seedstore. Previous data showed that whereas90-95% of the mature-seed embryos would germinate normally in vitro,only about 75% and 43% of the stage 8 and stage 7 embryos respectivelywould so germinate. The rates of shoot and root elongation—measures ofgermination vigor—had even greater sensitivity to developmental stage,these rates being reduced to 80% and 20% for the two immature stages.Germination was reduced to about 15% and zero, respectively, for the twoimmature stages after desiccation of the embryos to 10% moisturecontent. These data exemplify, for Douglas-fir, the large contrast inembryo quality between embryos at these stages of development, which iswell-known to those skilled in plant embryo development. In furthercontrast, quality of the somatic embryos, which were closest, but nottruly equivalent to, zygotic developmental stage 8, was characterized bysignificantly lower germination normalcy and vigor than the stage 7zygotic embryos. The genotype tested was representative of many somaticembryo genotypes.

Inspection of the scoreplot in FIG. 2A shows that these four populationsof contrasting embryo quality separate into four clearly distinct groupswhen plotted with respect to the first three principal components. Theembryo groups are: mature dry zygotics (black circles), August 14zygotics (inverted white triangles), July 23 zygotics (black squares)and genotype 1 somatics (“+” symbol). The centroid of the somatic embryogroup was shifted 8-10 standard deviations to the right along the PC1axis compared with all stages of zygotic embryos, which were separatedprimarily along the axes for PCs 2 and 3. Variability within the somaticembryos was much greater than within any of the zygotic embryo groups.

The loadings spectrum for PC1 (FIG. 2B, curve 20) contained mainly twopeaks, at 1450 and 1920 nm, attributable to water, indicating that thelarge separation and variability was due to a greater amount andvariability of somatic embryo water. In contrast, separation among thezygotic groups was mainly along PCs 2 (curve 22) and 3 (curve 24), whoseloadings spectra suggest a basis in greater lipid content (the doublepeak at 1720-1750 nm, and the peak at 2300 nm) for more mature embryos.Also, there are negative peaks around 1400 and 1900 nm that may have todo with hydrogen-bonded water. The somatic embryos were also separatedfrom the two more mature zygotic groups along the PC2 axis, due in partto their putative lower lipid concentration, as well as absorptiondifferences in the visible region. The percent of total spectralvariation accounted for by each PC was 84% for PC1, 8% for PC2, and 4%for PC3. Table 7 summarizes the quality of separation obtained among thefour embryo groups after principal component analyses of the spectraldata. The summary data tables for the various somatic embryoclassifications list the chemical features that are inferred to beassociated with specific wavelengths based upon the knownspectrophotometric behavior of that chemical class.

TABLE 7 Douglas-Fir Zygotic Embryos at Three Developmental StagesCompared With One Another, and With Somatic Embryos Immature ZygoticEmbryos Principal Wavelength/Inferred Stage 7 Stage 8 Mature SeedSomatic Components Chemical Features embryos embryos Embryos EmbryosNeeded Involved 15/15* 14/14* 8/9* 9/10* 1st Water (1450 nm + 1920 nm)(100%) (100%) (89%) (90%) 2nd Lipid (1700-1750 nm) 3rd Lipid + featureat 1890 nm Lipid (2300 nm) + feature at 1870 nm *Number correctlyclassified/number tested

The results with loblolly pine somatic and zygotic embryos are shown inFIG. 3A and Table 8. In this case, stage 8 zygotic embryos (blacksquares) and water-imbibed mature zygotic embryos (black triangles) arecompared with two genotypes of somatic embryos (genotype 5 denoted as“+” and genotype 7 denoted as “o”) pretreated by partial drying thencold. Somatic embryos were separated from zygotic embryos mainly by PC1,which, as in case of Douglas-fir embryos, was probably due to thesomatic embryos' higher water content relative to lipids (curve 26).Also, many loblolly pine somatic embryos were separated from zygoticembryos along PC2, which featured a dominant broad peak around 1800 nmof unknown source (curve 28). PC3 further distinguished the matureimbibed zygotic embryo group from the somatic embryo group, based on acombination of features, including a lipid (−ve) peak, pigmentation inthe visible region, and a small −ve peak around 1210 nm (which is aboutwhere the second overtone of C-H stretches in protein lie) shown incurve 30. Together, these three PCs accounted for 97% of variation inthe spectra (FIG. 3B). The percent of total spectral variation accountedfor by each PC was 92% for PC1 (curve 26), 4% for PC2 (curve 28), and 1%for PC3 (curve 30).

TABLE 8 Loblolly Pine Zygotic Embryos at Two Developmental Stages andLoblolly Pine Somatic Embryos Immature (stage 8) Mature PrincipalZygotic Zygotic Compo- Wavelength/ Embryos Embryos Somatic nentsInferred Chemical (Aug. 10) (October) Embryos Needed Features Involved10/10* 13/13* 28/29* 1st Water (1450 + 1920 nm) (100%) (100%) (97%) 2ndLipid (1700-1750 nm) 3rd 1800 nm broad peak Lipid (−ve 2300 nm) Protein(1210 nm) Lipid (1700-1750 nm) Pigments (400-500 nm) *Number correctlyclassified/number tested

Taken together, these data demonstrate that embryos can be accuratelyseparated by their NIR spectral characteristics into groups of differinggermination potential.

Principal Component Analysis of Spectra From Somatic Embryos of High-and Low-Quality Appearance

Ten cotyledonary-stage somatic embryos of high- and low-qualityappearance were selected from a single plate each of Douglas-fir(genotype 2) and loblolly pine (genotype 5) embryos, based upontraditional morphological indications of embryo quality, i.e.,morphologies that are most likely to result in a high or low frequencyof germination.

A summary of the separation obtained is presented in Table 9. ForDouglas-fir, it was possible to draw a straight line on the scoreplot ofPC3 versus PC1 that completely separated the high quality (“+”) and lowquality (black circles) groups (FIG. 4A). Most of this separationoccurred along the third PC (FIG. 4B, curve 32), which represented about2% of the overall variation. PC3 was distinguished in part by absorptionbands from pigments in the visible region, including chlorophyll. PC1(curve 34) represented about 96% of the total spectral variation.

TABLE 9 Cotyledon Stage Somatic Embryos With “High” vs. “Low” QualityMorphology Wavelength/ Inferred High Quality Low Quality PC's ChemicalMorphology Morphology Needed Features Douglas-Fir 10/10* 9/9* 1 Water(100%) (100%) (1450, 1920 nm) 3 Pigments in visible region shoulderfeature (1850-1920 nm) Loblolly Pine 9/10* 9/10* 1 Water  (90%)  (90%)(1450, 1920 nm) 3 Unknown (1400-1500 nm) Lipid (1710, 2300 nm) Boundwater (1870 nm) *Number correctly classified/number tested

FIG. 5A shows the scoreplot obtained from loblolly pine somatic embryoshaving high quality morphology (“+”) as compared to embryos having lowquality morphology (black circles). Almost complete (90%) separation wasachieved, with the first and third PCs combined. In the PC3 loadingsspectrum (FIG. 5B, curve 40), there was a strong, slightly bimodalnegative peak around 1450 nm (not water), plus putative lipid (1700 and2300 nm) and bound water (1870 nm) features, as well as absorption peaksin the visible region (380-600 nm). PC3 accounted for about 1% of thetotal spectral variation. PC1 (curve 36) represented about 95% of thetotal spectral variation and was mostly water. PCs 1 and 2 combined alsoprovided good separation, the PC2 loadings spectrum (curve 39) beingdominated by the shoulder feature between 1760 and 1900 nm. PC2accounted for about 3% of the total spectral variation. These resultsdemonstrate that principal component analysis of spectral data fromsomatic embryos having high- and low-quality morphological appearanceprovides a basis for developing a classification model that will allowsomatic embryos to be rapidly categorized with regards to theirgermination potential.

Principal Component Analysis of Spectra from Somatic Embryos in theCotyledon (Stage 8) and “Dome” (Stage 5) or “Just Cotyledon” (JC) (Stage6) Stages

Douglas-fir somatic embryos in two distinct developmental stages wereselected from plates of genotype 3. Somatic embryos in the cotyledonstage are known to have a much higher frequency of germination thansomatic embryos that are in the less mature “dome” or “justcotyledonary” (JC) developmental stages.

Dome/JC embryos (black circles in FIG. 6A) and cotyledonary (stage 8)embryos (“+”) that were plucked from the same plate formed two distinctgroups on a 3D scoreplot formed from PCs 1-3, such that only one embryoof the 19 just fell within the wrong group (FIG. 6A). The strongestcontributors to separation were PCs 1 (curve 42) and 2 (curve 44), whichare associated with (1) water and (2) lipid, possibly protein N-H,regions, plus the 1800 nm ‘shoulder’ feature, respectively (FIG. 6B).PCs 1 and 2 account for 82% and 9% of the total spectral variation,respectively, whereas PC 3 (curve 46) accounted for 4% of the totalspectral variation. Table 10 presents a summary of the accuracy of thespectral separations obtained using the cotyledon stage and “dome” or“just cotyledonary” stage somatic embryos.

TABLE 10 Cotyledon vs. Earlier Developmental Stages of Douglas-FirSomatic Embryos From Genotype 3 “Dome” or “Just PC's Wavelength/InferredCotyledon Stage Cotyledon” Stage Needed Chemical Features 10/10* 8/9* 1Water (100%) (89%) 2 Lipid (1700-1800 nm) Unknown (1420 nm) *Numbercorrectly classified/number tested

These results demonstrate that NIR spectral data can accuratelydistinguish between early developmental stages of somatic embryos, whichare germination-incompetent, and the final stage of development on petriplates (approximately equivalent to zygotic stage 8 embryos), many ofwhich are capable of germinating and producing seedlings.

Principal Component Analysis of Spectra From Cold-treated and ControlSomatic Embryos

Subjecting embryos to a 4-7° C. cold treatment on low-osmolality mediain the dark for 1-5 weeks may increase the frequency of subsequentembryo germination by 20 to 200%.

Principal component analysis of spectral data collected fromcold-treated and control Douglas-fir somatic embryos of two genotypes (3and 4) are presented in FIGS. 7A and 7B. In FIG. 7A solid black circlesor triangles identify cold-treated embryos for genotypes 3 and 4,respectively, and the corresponding open symbols identifynon-cold-treated embryos of the same two genotypes. For each genotype, astraight line can be drawn that will largely separate the twopopulations with the degree of success (from 79-100%) shown in Table 11.The separation was determined mainly by the PC2 axis, whose loadingsspectrum (FIG. 7B, curve 50) has both lipid and pigment components andaccounts for about 4% of the total spectral variation. PC1 (curve 48)accounts for about 91% of the spectral variation.

TABLE 11 Somatic Embryos That Have or Have Not Received Cold TreatmentSpecific Species and PC's Wavelength/Inferred Genotype ControlCold-treated Needed Chemical Features Douglas-Fir Genotype 3  9/10*10/10* 2 Lipids (1700-1750 nm) (90%) (100%) Shoulder region (1800-1900nm) Genotype 4 26/33*  9/10* 1 Water (79%)  (90%) Loblolly Pine Genotype5 19/20* 10/10* 1 Water (95%) (100%) Genotype 7 28/40* 17/20* 3 Lipid(1700-1750 nm) (70%)  (85%) 2 Shoulder region (1800-1900 nm) *Numbercorrectly classified/number tested

The results of principal component analysis for the equivalent contrastusing loblolly pine somatic embryos appears in FIGS. 8A and 8B. Loblollypine somatic embryos from genotype 5 (circles) exhibit a clearseparation of cold-treated (solid circles) and control groups (opencircles) in (FIG. 8A). Loblolly pine genotype 7 (triangles) exhibits asimilar tendency in regard to these two treatment groups. In general,embryos that were partially dried then cold-treated show higher, andgreater variation in, water contents than those that were not. Theseparations, for each genotype, were by PCs 1 and 2 combined, whichincorporate the water, lipid and 1800-1900 nm shoulder features notedfor Douglas-fir. PC1 (curve 52) and PC2 (curve 54) account for 92% and4% of the total spectral variation, respectively.

These results demonstrate that NIR spectral data can distinguish betweendevelopmentally similar (approx. stage 8) somatic embryos having highergermination potential (on account of prior cold or cold and partialdrying treatment) from those embryos of lower germination potential(having not received such treatments).

While the preferred embodiment of the invention has been illustrated anddescribed, it will be appreciated that various changes can be madetherein without departing from the spirit and scope of the invention.

The invention claimed is:
 1. A method for classifying plant embryosaccording to their germination potential, comprising: (a) developing aclassification model by (i) acquiring raw digital image data ofreference samples of whole plant embryos or of embryo organs of knowngermination potential; (ii) performing a data analysis by applying oneor more classification algorithms to the acquired raw digital imagedata, wherein at least one of the one or more classification algorithmsuses non-perimeter information, the non-perimeter information being morethan an embryo perimeter from the acquired raw digital image data, thedata analysis resulting in development of a classification model forclassifying plant embryos by their germination potential; (b) acquiringraw digital image data of a plant embryo or a plant embryo organ ofunknown germination potential; and (c) applying the developedclassification model to the raw digital image data of step (b) in orderto classify the plant embryo of unknown germination potential accordingto its presumed germination potential.
 2. The method according to claim1 wherein the raw digital image data acquired in step (a)(i) ispreprocessed using one or more preprocessing algorithms before step(a)(ii); the raw digital image data acquired in step (b) is preprocessedusing one or more preprocessing algorithms; and step (c) is carried outusing the preprocessed raw digital image data.
 3. The method accordingto claim 2 wherein the preprocessing algorithm removes raw image datathat is not from the plant embryo or plant embryo organ.
 4. The methodaccording to claim 2 wherein the preprocessing algorithm reduces theamount of raw image data yet retains substantially all of the embryo orembryo organ geometric information.
 5. The method according to claim 2wherein the preprocessing algorithm calculates metrics.
 6. The methodaccording to claim 1 wherein the raw digital image data is acquired frommore than one view of the plant embryo or plant embryo organ.
 7. Themethod according to claim 1 wherein the plant embryo is a plant somaticembryo.
 8. The method according to claim 1 wherein the plant is a tree.9. The method according to claim 8 wherein the tree is a member of theorder Coniferales.
 10. The method according to claim 8 wherein the treeis a member of the family Pinaceae.
 11. The method according to claim 8wherein the tree is selected from the group consisting of generaPseudotsuga and Pinus.
 12. The method according to claim 1 wherein thenon-perimeter information comprises a skewness coefficient.
 13. Themethod according to claim 1 wherein the non-perimeter informationcomprises a kurtosis coefficient.
 14. The method according to claim 1wherein the non-perimeter information comprises a mean value of a detailcoefficient.
 15. The method according to claim 1 wherein the at leastone of the one or more classification algorithms using non-perimeterinformation also uses perimeter information.
 16. The method according toclaim 15 wherein the perimeter information comprises perimeter radiifrom a top view of the reference samples.
 17. The method according toclaim 1 wherein the classification model is a single metricclassification model and the step of performing a data analysis byapplying one or more classification algorithms to the acquired rawdigital image data further comprises: (i) calculating a metric valuefrom the acquired raw digital image data of each embryo of knowngermination potential; (ii) dividing the metric values obtained in step(i) into two sets of metric values according to their known germinationpotential; (iii) calculating a Lorenz curve from the two sets of metricvalues; (iv) using any point on the Lorenz curve calculated in step(iii) as a threshold value to arrive at a single metric classificationmodel for classifying plant embryos by their germination potential. 18.The method according to claim 1 wherein the raw digital image datacomprises at least one of: absorption spectral raw data, transmittancespectral raw data, and reflectance spectral raw data.